{"id":"https://openalex.org/W7160953902","doi":"https://doi.org/10.48550/arxiv.2605.08897","title":"Shapley Regression for Rare Disease Diagnosis Support: a case study on APDS","display_name":"Shapley Regression for Rare Disease Diagnosis Support: a case study on APDS","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160953902","doi":"https://doi.org/10.48550/arxiv.2605.08897"},"language":"en","primary_location":{"id":"pmh:oai:HAL:hal-05619927v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-05619927","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IJCAI 2026 - International Joint Conference on Artificial Intelligence, Aug 2026, Bremen, Germany. ⟨10.48550/arXiv.2605.08897⟩","raw_type":"Conference papers"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://inria.hal.science/hal-05619927","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135967419","display_name":"Safa Alsaidi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alsaidi, Safa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135949587","display_name":"Tom\u00e1s Brogueira","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brogueira, Tom\u00e1s","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058861543","display_name":"Nizar Mahlaoui","orcid":"https://orcid.org/0000-0002-0030-8094"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahlaoui, Nizar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135934856","display_name":"Marc Vincent","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vincent, Marc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125754794","display_name":"Guilherme Pelegrina","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pelegrina, Guilherme","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135994129","display_name":"Nicolas Garcelon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garcelon, Nicolas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082641159","display_name":"Adrien Coulet","orcid":"https://orcid.org/0000-0002-1466-062X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Coulet, Adrien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066732273","display_name":"Miguel Couceiro","orcid":"https://orcid.org/0000-0003-2316-7623"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Couceiro, Miguel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11725","display_name":"Immunodeficiency and Autoimmune Disorders","score":0.5787000060081482,"subfield":{"id":"https://openalex.org/subfields/2403","display_name":"Immunology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11725","display_name":"Immunodeficiency and Autoimmune Disorders","score":0.5787000060081482,"subfield":{"id":"https://openalex.org/subfields/2403","display_name":"Immunology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11642","display_name":"Genomics and Rare Diseases","score":0.15209999680519104,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10261","display_name":"Genetic Associations and Epidemiology","score":0.06880000233650208,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.633400022983551},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6111999750137329},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.45590001344680786},{"id":"https://openalex.org/keywords/cohort","display_name":"Cohort","score":0.4196000099182129},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.35839998722076416},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.35190001130104065},{"id":"https://openalex.org/keywords/generalized-linear-model","display_name":"Generalized linear model","score":0.3393999934196472},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3287000060081482},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.32820001244544983}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.633400022983551},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6111999750137329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5788000226020813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4952999949455261},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44200000166893005},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.4196000099182129},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.38909998536109924},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3382999897003174},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3167000114917755},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.31049999594688416},{"id":"https://openalex.org/C87227347","wikidata":"https://www.wikidata.org/wiki/Q7100713","display_name":"Ordered logit","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C110313322","wikidata":"https://www.wikidata.org/wiki/Q7100793","display_name":"Ordinal regression","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C72134830","wikidata":"https://www.wikidata.org/wiki/Q5166524","display_name":"Convexity","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C2779701055","wikidata":"https://www.wikidata.org/wiki/Q929833","display_name":"Rare disease","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C201903717","wikidata":"https://www.wikidata.org/wiki/Q1778788","display_name":"Cohort study","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:HAL:hal-05619927v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-05619927","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IJCAI 2026 - International Joint Conference on Artificial Intelligence, Aug 2026, Bremen, Germany. ⟨10.48550/arXiv.2605.08897⟩","raw_type":"Conference papers"},{"id":"doi:10.48550/arxiv.2605.08897","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08897","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-05619927v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-05619927","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IJCAI 2026 - International Joint Conference on Artificial Intelligence, Aug 2026, Bremen, Germany. ⟨10.48550/arXiv.2605.08897⟩","raw_type":"Conference papers"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Activated":[0],"PI3K8":[1],"Syndrome":[2],"(APDS)":[3],"is":[4,27],"a":[5,77,86,121,142],"rare":[6],"genetic":[7],"immune":[8],"disorder":[9],"caused":[10],"by":[11,29,179],"variants":[12],"in":[13,46],"PIK3CD":[14],"or":[15],"PIK3R1,":[16],"with":[17,85,124,167],"highly":[18],"heterogeneous":[19],"symptoms":[20,94],"that":[21,120],"often":[22,66],"delay":[23],"diagnosis.":[24],"Early":[25],"recognition":[26],"hampered":[28],"overlapping":[30],"clinical":[31,180],"presentations":[32],"and":[33,99,134,160,169],"limited":[34],"clinician":[35],"awareness,":[36],"motivating":[37],"systematic,":[38],"data-driven":[39],"approaches":[40],"to":[41,141,164],"detect":[42],"APDS-associated":[43],"phenotypic":[44],"patterns":[45],"routine":[47],"electronic":[48],"health":[49],"records.":[50],"Traditional":[51],"linear":[52,83],"scoring":[53],"systems":[54],"cannot":[55],"capture":[56],"complex":[57],"symptom":[58],"interactions,":[59],"while":[60,95],"deep":[61],"learning":[62],"models,":[63],"though":[64],"expressive,":[65],"lack":[67],"interpretability.":[68],"To":[69],"bridge":[70],"this":[71],"gap,":[72],"we":[73],"propose":[74],"Shapley":[75,150],"regression,":[76],"novel":[78],"game-theoretic":[79],"model":[80,123],"replacing":[81],"the":[82,97,171],"predictor":[84],"k-additive":[87],"cooperative":[88],"game,":[89],"explicitly":[90],"modeling":[91],"co-occurrence":[92],"of":[93,101,110,145,173],"maintaining":[96],"transparency":[98],"convexity":[100],"logistic":[102],"regression.":[103],"We":[104,137],"carry":[105],"out":[106],"an":[107,128],"empirical":[108],"study":[109],"our":[111],"lightweight":[112],"method":[113],"on":[114,148],"eight":[115],"public":[116],"biomedical":[117],"datasets,":[118],"showing":[119],"2-additive":[122],"$l_{2}$":[125],"regularization":[126],"achieves":[127],"optimal":[129],"trade-off":[130],"between":[131,176],"predictive":[132],"power":[133],"noise":[135],"robustness.":[136],"also":[138],"apply":[139],"it":[140],"real-world":[143],"cohort":[144],"222":[146],"patients,":[147],"which":[149],"regression":[151],"accurately":[152],"distinguished":[153],"APDS":[154],"cases":[155],"from":[156],"matched":[157],"controls,":[158],"confirming":[159],"validating":[161],"phenotypes":[162],"known":[163],"be":[165],"associated":[166],"APDS,":[168],"facilitating":[170],"exploration":[172],"pairwise":[174],"interactions":[175],"symptoms,":[177],"validated":[178],"experts.":[181]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-13T00:00:00"}